Pattern Inspection Model Using Gaussian Vector Clustering
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Solution Overview
Problem
In semiconductor manufacturing, as critical dimensions decrease, detecting yield-related problems and minimizing problem-solving time become increasingly challenging due to the complexity of pattern inspection in semiconductor devices.
Innovation Solution
A pattern inspection method that converts sample patterns into training patterns, extracts feature values, sets feature vectors, converts them into Gaussian vectors, clusters these vectors, selects select vectors, and stores them as a pattern model in a database, allowing for precise inspection of target patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pattern inspection methods are used, then inspection process is simple, but inspection precision deteriorates as critical dimension decreases
Solution Approach 1:
The patent introduces a pattern model as an intermediary between the inspection system and the actual pattern inspection. The pattern model is created by clustering sample patterns and their feature vectors, then used to convert target patterns into inspection patterns. This intermediary structure enables precise inspection of complex patterns without requiring the inspection system itself to be overly complex, as the pattern model handles the complexity of pattern recognition and classification.
2Reliability
If more sample patterns are inspected to improve model accuracy, then inspection reliability improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing sample patterns and creating the pattern model before actual inspection. During the model creation phase, sample patterns are converted to training patterns, feature vectors are extracted and clustered, and the pattern model is built and stored in a database. This preliminary processing allows the actual inspection to use the pre-built model for rapid comparison and classification, significantly reducing processing time while maintaining high reliability through the comprehensive use of clustered sample data.
3Measurement precision
If traditional feature extraction methods are used, then processing is fast, but measurement precision deteriorates for complex patterns
Solution Approach 1:
The patent transforms the feature extraction process by converting patterns into multi-dimensional feature vectors and then applying clustering algorithms to create a pattern model in a higher-dimensional space. Instead of directly comparing raw pattern data, the system extracts multiple features (such as shape, size, position) to create comprehensive feature vectors, clusters these vectors to identify pattern categories, and then uses the clustered model for inspection. This dimensional transformation enables precise measurement of complex patterns while maintaining processing efficiency through the structured approach of feature extraction followed by clustering-based classification.
Data Source
AI summary
A pattern inspection method includes converting sample patterns of a sample image into training images, extracting feature values of the training patterns, setting feature vectors of the training patterns on the basis of the feature values, converting the feature vectors into Gaussian vectors, clustering the Gaussian vectors, thereby sorting the Gaussian vectors into clusters, selecting a select vector from each of the clusters, storing, as a pattern model, the training pattern corresponding to the select vector in a database, converting a target pattern of a target image into an inspection pattern on the basis of the pattern model, and inspecting the inspection pattern.